Image segmentation is one of the most important and difficult tasks in digital image processing and image analysis. It represents a key stage of automated image analysis and interpretation. Segmentation algorithms for gray-scale images utilize basic properties of intensity values such as discontinuity and similarity. Detection of discontinuities in gray-scale images is typically based on spatial masks whose response at any pixel in the image can be thought of as a finite difference approximation of a differential operator. A mask, which can be used in a typical spatial feature extraction techniques and may also be referred to as kernel, can be realized as a function that operates on pixel values in a predefined neighborhood M×N of a pixel with spatial coordinates (i, j). Examples of some of the most popular gray-scale edge detectors include Canny, Sobel, and Prewitt detectors. However, it is possible to enhance edge-detection capability by means of using spectral information provided by multispectral (MS) or hyperspectral (HS) imagery. A multi-spectral imagery captures image data at specific frequencies across the electromagnetic spectrum. Hyperspectral imagery captures image data from across the electromagnetic spectrum.
Transition from a gray-scale to a multi-color image complicates edge detection significantly. Standard definition of a gray-scale edge as a “ramp,” or “ridge” between two regions is no longer appropriate since a multi-color image has multiple image planes, one for each spectral band. More importantly, depending upon the scene, two distinct regions may exhibit the same intensity for one or more bands. In other words, with respect to such iso-luminant bands, the edge between the two regions is characterized by a jump in color rather than intensity. Clearly, iso-luminant edges cannot be detected by a standard gradient operator. Extension of other gray-scale processing techniques to multi-colored images, such as those based on differential operators, faces similar difficulties.
Extension of differential edge detection to multi-color images has followed two principal paths. A straightforward approach is to apply differential operators such as the gradient separately to each image plane and then somehow integrate this information to obtain edge and segmentation information. It has been previously pointed out that this can result in undesirable segmentation because information in separate channels is treated as independent whereas in actuality it is not. A second approach to multi-color edge detection is to embed the variations of all color channels in a single measure, which is then used to obtain the edge maps. Typically, this approach is developed by starting from a given gray-scale operator, which is then extended consistently to multi-color images. By “consistently” it is mean that the extended multi-color operator reduces to its original gray-scale prototype when applied to a single color image. Two representative examples of this approach are the multi-color gradient (MCG) and the morphological color gradient (MoCG). While multi-color gradient and related ideas have been used with great success computation of the multi-color gradient for multi-color images with large numbers of bands can be quite expensive.